Keywords: e-cigarette prevention, youth nicotine use prevention, public health surveillance, evidence-based communication, social media analysis

Introduction: Between 2013 and 2024, youth e-cigarette use rose 400%, prompting government agencies and public health organizations to launch prevention campaigns. Many campaigns encountered intense social media opposition, with some forced to halt. Such opposition undermines campaign efficacy, allowing for the spread of misinformation about e-cigarettes among youth. Understanding opposition tactics across evolving social media platforms is critical not only for protecting public health and establishing evidence-based prevention practices, but also for combatting misinformation in political campaigns and policy advocacy. Grounded in the theoretical frameworks of networked gatekeeping, principal-agent theory, and algorithmic performance, this study examines counter-campaign tactics across platforms, from coordinated counter-advocacy/astroturfing on X/Twitter to algorithm-driven opposition on TikTok.

Methods: We used a mixed-methods approach to analyze two datasets: (1) 279,572 X/Twitter posts classified as relevant to 14 different e-cigarette prevention campaigns (2014-2020), retrieved using Twitter’s now-deprecated Historical PowerTrack API, and (2) TikTok videos mentioning prevention campaign hashtags (2020-2023, data collection ongoing)1, collected via TikTok’s official Research API. We captured prevention campaigns at national (e.g., US Food and Drug Administration’s “The Real Cost”), state (e.g., California’s “Still Blowing Smoke”), and local (e.g., Chicago’s “Vaping Truth”) levels. Machine learning classification (F1=0.90) and human labeling by five trained researchers (Gwet's AC1>0.81) ensured robust analysis of posts. Analyses included message volume, content characteristics, engagement metrics, and geolocation.

Results: Opposition dynamics differed significantly across platforms. On X/Twitter, national campaigns faced moderate opposition (20-27% of posts), while local efforts encountered intense opposition (up to 99%), with geolocated tweets primarily originating from outside campaign jurisdictions (95.7%). The potential reach of prevention/neutral X/Twitter messages was 92% higher than the reach of opposition messages (732M vs. 381M users), despite the opposition’s coordinated amplification strategies. On TikTok, opposition (14.8%) leveraged algorithm-amplified entertainment content (66.4%), and overall, Tiktok users were individual creators (87.2%), while the presence of public health organizations remained negligible (1.5%). Inconsistent campaign branding enabled opposition forces to co-opt campaign identifiers, undermining message authenticity and campaign credibility. Cross-platform analysis identified four persistent opposition strategies: undermining authorities, misrepresenting health claims, normalizing e-cigarette use, and challenging evidence-based messaging.

Conclusions: Our study highlights how opposition to e-cigarette use prevention campaigns evolves across social media platforms, illustrating how counter-campaign strategies adapt to platform-specific affordances and user behaviors. Opposition groups effectively exploit platform architectures to amplify their narratives, influencing public discourse. These findings underscore the need for platform-specific prevention strategies that maintain campaign branding, leverage youth-preferred content formats, and increase public health presence. Systematic monitoring and elevating evidence-based content from public health entities could better counter misinformation where youth engage with health information. Beyond e-cigarette prevention, this research provides critical insights into how social media’s algorithmic and network functionalities are leveraged to shape public opinion and policy discourse.